# from __future__ import annotations
#
# import torch
#
# import sgm.models.diffusion
# import sgm.modules.diffusionmodules.denoiser_scaling
# import sgm.modules.diffusionmodules.discretizer
# from modules import devices, shared, prompt_parser
# from modules import torch_utils
#
# from backend import memory_management
#
#
# def get_learned_conditioning(self: sgm.models.diffusion.DiffusionEngine, batch: prompt_parser.SdConditioning | list[str]):
#
#     for embedder in self.conditioner.embedders:
#         embedder.ucg_rate = 0.0
#
#     width = getattr(batch, 'width', 1024) or 1024
#     height = getattr(batch, 'height', 1024) or 1024
#     is_negative_prompt = getattr(batch, 'is_negative_prompt', False)
#     aesthetic_score = shared.opts.sdxl_refiner_low_aesthetic_score if is_negative_prompt else shared.opts.sdxl_refiner_high_aesthetic_score
#
#     devices_args = dict(device=self.forge_objects.clip.patcher.current_device, dtype=memory_management.text_encoder_dtype())
#
#     sdxl_conds = {
#         "txt": batch,
#         "original_size_as_tuple": torch.tensor([height, width], **devices_args).repeat(len(batch), 1),
#         "crop_coords_top_left": torch.tensor([shared.opts.sdxl_crop_top, shared.opts.sdxl_crop_left], **devices_args).repeat(len(batch), 1),
#         "target_size_as_tuple": torch.tensor([height, width], **devices_args).repeat(len(batch), 1),
#         "aesthetic_score": torch.tensor([aesthetic_score], **devices_args).repeat(len(batch), 1),
#     }
#
#     force_zero_negative_prompt = is_negative_prompt and all(x == '' for x in batch)
#     c = self.conditioner(sdxl_conds, force_zero_embeddings=['txt'] if force_zero_negative_prompt else [])
#
#     return c
#
#
# def apply_model(self: sgm.models.diffusion.DiffusionEngine, x, t, cond, *args, **kwargs):
#     if self.model.diffusion_model.in_channels == 9:
#         x = torch.cat([x] + cond['c_concat'], dim=1)
#
#     return self.model(x, t, cond, *args, **kwargs)
#
#
# def get_first_stage_encoding(self, x):  # SDXL's encode_first_stage does everything so get_first_stage_encoding is just there for compatibility
#     return x
#
#
# sgm.models.diffusion.DiffusionEngine.get_learned_conditioning = get_learned_conditioning
# sgm.models.diffusion.DiffusionEngine.apply_model = apply_model
# sgm.models.diffusion.DiffusionEngine.get_first_stage_encoding = get_first_stage_encoding
#
#
# def encode_embedding_init_text(self: sgm.modules.GeneralConditioner, init_text, nvpt):
#     res = []
#
#     for embedder in [embedder for embedder in self.embedders if hasattr(embedder, 'encode_embedding_init_text')]:
#         encoded = embedder.encode_embedding_init_text(init_text, nvpt)
#         res.append(encoded)
#
#     return torch.cat(res, dim=1)
#
#
# def tokenize(self: sgm.modules.GeneralConditioner, texts):
#     for embedder in [embedder for embedder in self.embedders if hasattr(embedder, 'tokenize')]:
#         return embedder.tokenize(texts)
#
#     raise AssertionError('no tokenizer available')
#
#
#
# def process_texts(self, texts):
#     for embedder in [embedder for embedder in self.embedders if hasattr(embedder, 'process_texts')]:
#         return embedder.process_texts(texts)
#
#
# def get_target_prompt_token_count(self, token_count):
#     for embedder in [embedder for embedder in self.embedders if hasattr(embedder, 'get_target_prompt_token_count')]:
#         return embedder.get_target_prompt_token_count(token_count)
#
#
# # those additions to GeneralConditioner make it possible to use it as model.cond_stage_model from SD1.5 in exist
# sgm.modules.GeneralConditioner.encode_embedding_init_text = encode_embedding_init_text
# sgm.modules.GeneralConditioner.tokenize = tokenize
# sgm.modules.GeneralConditioner.process_texts = process_texts
# sgm.modules.GeneralConditioner.get_target_prompt_token_count = get_target_prompt_token_count
#
#
# def extend_sdxl(model):
#     """this adds a bunch of parameters to make SDXL model look a bit more like SD1.5 to the rest of the codebase."""
#
#     dtype = torch_utils.get_param(model.model.diffusion_model).dtype
#     model.model.diffusion_model.dtype = dtype
#     model.model.conditioning_key = 'crossattn'
#     model.cond_stage_key = 'txt'
#     # model.cond_stage_model will be set in sd_hijack
#
#     model.parameterization = "v" if isinstance(model.denoiser.scaling, sgm.modules.diffusionmodules.denoiser_scaling.VScaling) else "eps"
#
#     discretization = sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization()
#     model.alphas_cumprod = torch.asarray(discretization.alphas_cumprod, device=devices.device, dtype=torch.float32)
#
#     model.conditioner.wrapped = torch.nn.Module()
#
#
# sgm.modules.attention.print = shared.ldm_print
# sgm.modules.diffusionmodules.model.print = shared.ldm_print
# sgm.modules.diffusionmodules.openaimodel.print = shared.ldm_print
# sgm.modules.encoders.modules.print = shared.ldm_print
#
# # this gets the code to load the vanilla attention that we override
# sgm.modules.attention.SDP_IS_AVAILABLE = True
# sgm.modules.attention.XFORMERS_IS_AVAILABLE = False
